Winect: 3D Human Pose Tracking for Free-form Activity Using Commodity WiFi
Winect: 3D Human Pose Tracking for Free-form Activity Using Commodity WiFi
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DOI：
10.1145/3494973
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Yili Ren;Z. Wang;Sheng Tan;Yingying Chen;Jie Yang
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文献类型：
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作者：
Yili Ren;Z. Wang;Sheng Tan;Yingying Chen;Jie Yang
WiFi人类感知在支持新兴的人机交互应用方面变得越来越有吸引力。相应的技术已经逐渐从对多种活动类型的分类发展到对3D人体姿势的更细粒度的跟踪。然而，现有的基于WiFi的3D人体姿势跟踪仅限于一组预定义的活动。在这项工作中，我们介绍了Winect，一个使用商用WiFi设备进行自由形式活动的3D人体姿势跟踪系统。我们的系统通过估计由人体的一组关节组成的3D骨架姿势来跟踪自由形式的活动。特别是，我们将信号分离和关节运动建模相结合，实现了自由形式的动作跟踪。我们的系统首先利用人体反射信号的二维到达角来识别运动的肢体，并分离出每条肢体的纠缠信号。然后，它跟踪每条肢体，并通过对肢体运动和相应关节之间的内在关系进行建模，构建身体的3D骨架。我们的评估结果表明，Winect是独立于环境的，在包括非视距(NLOS)场景在内的各种具有挑战性的环境中，可以实现厘米级的自由形式活动跟踪。
WiFi human sensing has become increasingly attractive in enabling emerging human-computer interaction applications. The corresponding technique has gradually evolved from the classification of multiple activity types to more fine-grained tracking of 3D human poses. However, existing WiFi-based 3D human pose tracking is limited to a set of predefined activities. In this work, we present Winect, a 3D human pose tracking system for free-form activity using commodity WiFi devices. Our system tracks free-form activity by estimating a 3D skeleton pose that consists of a set of joints of the human body. In particular, we combine signal separation and joint movement modeling to achieve free-form activity tracking. Our system first identifies the moving limbs by leveraging the two-dimensional angle of arrival of the signals reflected off the human body and separates the entangled signals for each limb. Then, it tracks each limb and constructs a 3D skeleton of the body by modeling the inherent relationship between the movements of the limb and the corresponding joints. Our evaluation results show that Winect is environment-independent and achieves centimeter-level accuracy for free-form activity tracking under various challenging environments including the none-line-of-sight (NLoS) scenarios.